Results 21 to 30 of about 5,586 (174)
Performance of Multi-Armed Bandit Algorithms in Dynamic vs. Static Environments: A Comparative Analysis [PDF]
This paper conducts a comparative analysis of Multi-Armed Bandit (MAB) algorithms, particularly the Upper Confidence Bound (UCB) and Thompson Sampling (TS) algorithms, and focuses on the performance of these algorithms in both static and dynamic ...
Zhao Boxi
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Computer experiments are widely used to mimic expensive physical processes as black-box functions. A typical challenge of expensive computer experiments is to find the set of inputs that produce the desired response.
Rajitha Meka +5 more
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Efficient crowdsourcing of unknown experts using multi-armed bandits [PDF]
We address the expert crowdsourcing problem, in which an employer wishes to assign tasks to a set of available workers with heterogeneous working costs.
Tran-Thanh, Long +11 more
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Multi-Armed Bandits and Quantum Channel Oracles [PDF]
Multi-armed bandits are one of the theoretical pillars of reinforcement learning. Recently, the investigation of quantum algorithms for multi-armed bandit problems was started, and it was found that a quadratic speed-up (in query complexity) is possible ...
Simon Buchholz +2 more
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Differential Privacy in Social Networks Using Multi-Armed Bandit
There has been an exponential growth over the years in the number of users connected to social networks. This has spurred research interest in social networks to ensure the privacy of users. From a theoretical standpoint, a social network is modeled as a
Olusola T. Odeyomi
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Addictive Games: Case Study on Multi-Armed Bandit Game
The attraction of games comes from the player being able to have fun in games. Gambling games that are based on the Variable-Ratio schedule in Skinner’s experiment are the most typical addictive games.
Xiaohan Kang +3 more
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Epsilon–First Policies for Budget–Limited Multi-Armed Bandits
We introduce the budget–limited multi–armed bandit (MAB), which captures situations where a learner’s actions are costly and constrained by a fixed budget that is incommensurable with the rewards earned from the bandit machine, and then describe a first ...
Munoz de Cote, Enrique +5 more
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On-Line Adaptation of Exploration in the One-Armed Bandit with Covariates Problem
Many sequential decision making problems require an agent to balance exploration and exploitation to maximise long-term reward. Existing policies that address this tradeoff typically have parameters that are set a priori to control the amount of ...
Niall M. Adams +8 more
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Optimizing Coupon Recommendation Using Multi-Armed Bandit Algorithms [PDF]
In recent years, coupon recommendations have become an essential strategy for e-commerce platforms to attract users and increase transaction volume.
Guo Jun
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StreamingBandit: Experimenting with Bandit Policies
A large number of statistical decision problems in the social sciences and beyond can be framed as a (contextual) multi-armed bandit problem. However, it is notoriously hard to develop and evaluate policies that tackle these types of problems, and to use
Jules Kruijswijk +3 more
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